Rainfall-Induced Runoff from Exposed Streambed Sediments: An Important Source of Water Pollution
Bibliographic record
Abstract
When surface water levels decline, exposed streambed sediments can be mobilized and washed into the water course when subjected to erosive rainfall. In this study, rainfall simulations were conducted over exposed sediments along stream banks at four distinct locations in an agriculturally dominated river basin with the objective of quantifying the potential for contaminant loading from these often overlooked runoff source areas. At each location, simulations were performed at three different sites. Nitrogen, phosphorus, sediment, fecal indicator bacteria, pathogenic bacteria, and microbial source tracking (MST) markers were examined in both prerainfall sediments and rainfall-induced runoff water. Runoff generation and sediment mobilization occurred quickly (10-150 s) after rainfall initiation. Temporal trends in runoff concentrations were highly variable within and between locations. Total runoff event loads were considered large for many pollutants considered. For instance, the maximum observed total phosphorus runoff load was on the order of 1.5 kg ha. Results also demonstrate that runoff from exposed sediments can be a source of pathogenic bacteria. spp. and spp. were present in runoff from one and three locations, respectively. Ruminant MST markers were also present in runoff from two locations, one of which hosted pasturing cattle with stream access. Overall, this study demonstrated that rainfall-induced runoff from exposed streambed sediments can be an important source of surface water pollution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".